3 citations · 3 across the 3 of their papers we have counts for
3 papers
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
Ferdinand Kahenga, Antoine Bagula, Patrick Sello +1
Federated learning in practice must contend with heterogeneous feature spaces, severe non-IID data, and scarce labels across clients. We present FedFusion, a federated transfer-lea…
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
Ferdinand Kahenga, Antoine Bagula, Sajal K. Das +1
Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges…
Modelling DDoS Attacks in IoT Networks using Machine Learning
Pheeha Machaka, Olasupo Ajayi, Hloniphani Maluleke +3
In current Internet-of-Things (IoT) deployments, a mix of traditional IP networking and IoT specific protocols, both relying on the TCP protocol, can be used to transport data from…